Abdelelah Alzahed

Abdelelah Alzahed

Royal Military College of Canada

H-index: 8

North America-Canada

Professor Information

University

Royal Military College of Canada

Position

Postdoctoral Fellow

Citations(all)

156

Citations(since 2020)

145

Cited By

113

hIndex(all)

8

hIndex(since 2020)

8

i10Index(all)

7

i10Index(since 2020)

4

Email

University Profile Page

Royal Military College of Canada

Research & Interests List

Antennas

EM

Fields

ML and AI

Testing

Top articles of Abdelelah Alzahed

Development of a 6 GHz RF-EMF Exposure System for Investigating Human Skin Temperature Responses: Characterization, Integration, and Pilot Testing

We developed a radiofrequency electromagnetic field (RF-EMF) exposure system to investigate human skin temperature responses to localized exposures. The system was designed to project a 6 GHz RF-EMF beam with enough energy to rapidly increase peak local skin temperature on the human forearm from a baseline of 30–32°C to 38°C within 6 min. First, the RF-EMF exposure conditions were characterized using computer simulations to confirm that the antenna produced the desired spot size (4 cm) and resultant temperature rise in the skin. ANSYS-HFSS and Sim4Life electromagnetic and thermal simulations were performed to fully characterize the relation between electromagnetic physics and the bioheat thermal conduction problem. Next, an open-ended waveguide antenna was integrated with other hardware peripherals to comprise the full RF-EMF exposure system. Finally, human pilot testing was …

Authors

Abdelelah Alzahed,Eric Lemay,Mykola Zhuk,Gregory B Gajda,James P Mcnamee,Gregory W Mcgarr

Journal

IEEE Access

Published Date

2023/9/11

An Electromagnetic Neural Network for Inverse Source Modeling of Wire-Like Objects

We propose a new electromagnetic neural network to extract the spatial features of wire objects via a novel spatial singularity expansion method (S-SEM). The proposed approach utilizes a recently-found radiation function that holds the spatial parameters of targets defined as the surface current and the geometrical details. Through EM machine learning, an estimation of these parameters is performed in a form of inverse problems for a single wire system. The estimated parameters, which are the S-SEM's poles and strengths, are validated and compared with numerical results obtained from the EM solver where an excellent agreement is observed.

Authors

Abdelelah M Alzahed,Yahia MM Antar,Said Mikki

Published Date

2020/7/5

Electromagnetic machine learning for inverse modeling using the spatial singularity expansion method

In this article, we propose a general technique that utilizes a unified electromagnetic and machine learning (ML) technique for inverse modeling and antenna characterization. The recently developed spatial singularity expansion method (S-SEM) is deployed to explicate the electromagnetic behavior of antennas in the form of an accurate digital signal processing (DSP) model. An ML framework is devised and combined with the S-SEM-based DSP model to design a novel inverse source modeling algorithm. The combined S-SEM-ML system departs from the state-of-the-art approaches in being capable of processing far-field data in order to jointly estimate the surface current distribution on the examined radiators, in addition to reconstructing their geometrical details. Various straight and bent wire systems are investigated, including single and multiple array configurations. A study on the impact of additive free-space …

Authors

A. Alzahed,S. Mikki,Y. Antar

Journal

IEEE Journal on Multiscale and Multiphysics Computational Techniques

Published Date

2020/2/12

A spatial SEM-based shallow neural network for electromagnetic inverse source modeling

We derive and verify a new type of low-complexity neural networks using the recently introduced spatial singularity expansion method (S-SEM). The neural network consists of a single layer (Shallow Learning approach to machine learning) but with its activation function replaced by specialized S-SEM radiation mode functions derived by electromagnetic theory. The proposed neural network can be trained by measured near-or far-field data, eg, RCS, probe-measured fields, array manifold samples, in order to reproduce the unknown source current on the radiating structure. We apply the method to wire structures and show that the various spatial resonances of the radiating current can be very efficiently predicted by the S-SEM-based neural network. Convergence results are compared with Genetic Algorithms and are found to be considerably superior in speed and accuracy.

Authors

Abdelelah Alzahed,Said Mikki,Yahia M Antar

Journal

Progress In Electromagnetics Research M

Published Date

2020

Design of neural-network-based mutual coupling compensators for printed planar antenna arrays

We propose a methodology to compensate the effect of mutual coupling in planar antenna array arrangements using a novel electromagnetic machine learning (ML) technique. The electromagnetic behavior of the array elements and their mutual coupling interactions are described via the antenna current Green's function (ACGF). A ML artificial neural network (ANN) procedure is then applied and combined with the ACGF to optimize array ports and mitigate for mutual coupling effects.

Authors

Abdelelah M Alzahed,Yahia MM Antar,Said Mikki

Published Date

2020/7/5

Professor FAQs

What is Abdelelah Alzahed's h-index at Royal Military College of Canada?

The h-index of Abdelelah Alzahed has been 8 since 2020 and 8 in total.

What are Abdelelah Alzahed's research interests?

The research interests of Abdelelah Alzahed are: Antennas, EM, Fields, ML and AI, Testing

What is Abdelelah Alzahed's total number of citations?

Abdelelah Alzahed has 156 citations in total.

What are the co-authors of Abdelelah Alzahed?

The co-authors of Abdelelah Alzahed are Said Mikki.

Co-Authors

H-index: 26
Said Mikki

Said Mikki

Zhejiang University

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